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Record W3200020064 · doi:10.2478/ijmbr-2021-0005

Digital Technologies and Music Digitisation: Challenges and Opportunities for the Nepalese Music Industry

2021· article· en· W3200020064 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Music Business Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSociopolitical Dynamics in Nepal
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMusic industryDigital audioBusiness modelDigital eraBusinessMarketingEngineeringVisual artsMusic educationTelecommunicationsComputer scienceThe InternetArtWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract This paper investigates the current legitimate digital music business trends and models created by the innovation of new digital technologies and examines their pertinence in the Nepalese music industry. Further, it scrutinises neighbouring music markets and juxtaposes the Nepalese music market against their current market trends. Based on eight in-depth semi-structured interviews with executives and stakeholders of different major, medium and independent Nepalese record labels, the paper examines two questions: what is preventing Nepalese recorded music from being found digitally and accessible legally; and what are the opportunities, gaps and requirements that confront the search for a commercially viable route for the optimal digital music business model to make Nepalese music digitally and legally accessible, both locally and globally?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.341
GPT teacher head0.425
Teacher spread0.084 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it